Analysis of Time-Series Gene Expression Data to Explore Mechanisms of Chemical-Induced Hepatic Steatosis Toxicity.

Non-alcoholic fatty liver disease (NAFLD) represents a wide spectrum of disease, ranging from simple fatty liver through steatosis with inflammation and necrosis to cirrhosis. One of the most challenging problems in biomedical research and within the chemical industry is to understand the underlying mechanisms of complex disease, and complex adverse outcome pathways (AOPs). Based on a set of 28 steatotic chemicals with gene expression data measured on primary hepatocytes at three times (2, 8, and 24 h) and three doses (low, medium, and high), we identified genes and pathways, defined as molecular initiating events (MIEs) and key events (KEs) of steatosis using a combination of a time series and pathway analyses.

Investigation of ifosfamide and chloroacetaldehyde renal toxicity through integration of in vitro liver-kidney microfluidic data and pharmacokinetic-system biology models.

We have integrated in vitro and in silico data to describe the toxicity of chloroacetaldehyde (CAA) on renal cells via its production from the metabolism of ifosfamide (IFO) by hepatic cells. A pharmacokinetic (PK) model described the production of CAA by the hepatocytes and its transport to the renal cells. A system biology model was coupled to the PK model to describe the production of reactive oxygen species (ROS) induced by CAA in the renal cells.

First pass intestinal and liver metabolism of paracetamol in a microfluidic platform coupled with a mathematical modeling as a means of evaluating ADME processes in humans.

We developed a microfluidic platform to investigate paracetamol intestinal and liver first pass metabolism. This approach was coupled with a mathematical model to estimate intrinsic in vitro parameters and to predict in vivo processes. The kinetic modeling estimated the paracetamol and paracetamol sulfate permeabilities, the sulfate and glucuronide effluxes in the intestine compartment.

Occupational exposure to cobalt: a population toxicokinetic modeling approach validated by field results challenges the biological exposure index for urinary cobalt.

This study modeled the urinary toxicokinetics of cobalt exposure based on 507 urine samples from 16 workers, followed up for 1 week, and 108 related atmospheric cobalt measurements to determine an optimal urinary cobalt sampling strategy at work and a corresponding urinary exposure threshold (UET). These data have been used to calibrate a population toxicokinetic model, taking into account both the measurement uncertainty and intra- and interindividual variability. Using the calibrated model, urinary sampling sensitivity and specificity performance in detecting exposure above the 20 microg/m(3) threshold limit value - time-weighted average (TLV-TWA) has been applied to identify an optimal urine sampling time.

Stochasticity in physiologically based kinetics models: implications for cancer risk assessment.

In case of low-dose exposure to a substance, its concentration in cells is likely to be stochastic. Assessing the consequences of this stochasticity in toxicological risk assessment requires the coupling of macroscopic dynamics models describing whole-body kinetics with microscopic tools designed to simulate stochasticity. In this article, we propose an approach to approximate stochastic cell concentration of butadiene in the cells of diverse organs.